• Corpus ID: 251979408

Understanding the dynamic impact of COVID-19 through competing risk modeling with bivariate varying coefficients (preprint)

@inproceedings{Wu2022UnderstandingTD,
  title={Understanding the dynamic impact of COVID-19 through competing risk modeling with bivariate varying coefficients (preprint)},
  author={Wenbo Wu and John D. Kalbfleisch and J. M. Taylor and Jian Kang and Kevin He},
  year={2022}
}

• Wenbo Wu, J. Kalbfleisch, +2 authors Kevin He
• Published 1 September 2022
• Medicine

The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. Motivated by a request by the U.S. Centers for Medicare &Medicaid Services, our analysis of their postdischarge hospital readmissions and deaths in 2020 revealed that the COVID-19 effect has varied significantly with postdischarge time and time since the onset of the pandemic. However, the complex dynamics of the COVID-19 effect trajectories cannot be characterized by existing varying coefficient models. To address this issue, we propose a bivariate varying coefficient model for competing risks within a cause-specific hazard framework, where tensor-product B-splines are used to estimate the surface of the COVID-19 effect. An efficient proximal Newton algorithm is developed to facilitate the fitting of the new model to the massive Medicare data for dialysis patients. Difference-based anisotropic penalization is introduced to mitigate model overfitting and the wiggliness of the estimated trajectories;various cross-validation methods are considered in the determination of optimal tuning parameters. Hypothesis testing procedures are designed to examine whether the COVID-19 effect varies significantly with postdischarge time and the time since pandemic onset, either jointly or separately. Simulation experiments are conducted to evaluate the estimation accuracy, type I error rate, statistical power, and model selection procedures. Applications to Medicare dialysis patients demonstrate the real-world performance of the proposed methods.